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From |
Tomas M <anon556656@live.ca> |

To |
<statalist@hsphsun2.harvard.edu> |

Subject |
RE: st: RE: Quantile regression with stata |

Date |
Fri, 20 Feb 2009 09:26:58 -0800 |

Thank you, thank you, thank you. I am amazed at the quality of responses, it is like having a personal tutor with Stata. Thank you everyone for your input and help. ---------------------------------------- > Subject: st: RE: Quantile regression with stata > Date: Fri, 20 Feb 2009 12:51:04 +0000 > From: n.j.cox@durham.ac.uk > To: statalist@hsphsun2.harvard.edu > > Some further comments embedded below. > > Nick > n.j.cox@durham.ac.uk > > Tomas M > > I am using quantile regression to model the 50th percentile for my data. > Unfortunately, the resources are limited on qreg when comparing to the > literature available for traditional regression models. > > Questions: > > 1. I am mainly focused on the 50th percentile. But, if I wanted to > compare 25th and 75th models (using the sreg with q(0.25 0.50 0.75) > option), I am wondering if it is better to use the same set of > predictors for each percentile, or if I should use a different set of > predictors for each percentile? I wonder about this since each > percentile may have a different set of significant predictors (for > example, age may be significant for the 50th percentile, but not > significant for the 25th percentile). Thus, is it better to compare > models for 25th 50th and 75th percentiles using the best fitted model > with all relevant significant predictors? > >>>> This is general modelling strategy and not specific to -qreg-. If > you were say a graduate student of mine I'd personally rather see the > same set of predictors being used in all models to be compared. > Otherwise it's a matter of speculation how predictors left out of a > model would have performed if included. I don't see that a model need > include only predictors individually declared significant: that's > putting more reliance on the machinery than is deserved. However, I > would have no objection to also seeing slimmed down models. Other styles > and tastes prevail too. > > 2. My other question pertains to interpretation of coefficients. When > I run a model with certain predictors, sometimes I get a very small > coefficient (i.e. 5e-15). How do I interpret this, and what does this > mean? I do notice that this disappears once I collapse the categories > for the predictor. > >>>> As above. It means as much or as little as it says. Without further > evidence, it looks small, but all depends on what the units of > measurement are and on looking at t-statistics as well and on > considering what else in the model. If some categorical variable is > represented by a bunch of indicators there is a very good case for > keeping them all even if some aren't significant. > > 3. What tools are available to assess goodness of fit for my qreg model? > I have read through the qreg postestimation commands for stata, and it > seems that linktest, and predict would be my only options (i.e. plots of > residuals versus fitted values are available). I have also looked > through the UCLA regression with stata web book section on quantile > regression, and it also states that there are limited postestimation > commands available. > >>>> That is part illusion. You need not be restricted to canned > commands. Indeed if you can get residuals and fitted you can get many > other things too. Note that the -modeldiag- package (-search- for > locations) includes several graphical commands that both make sense and > work after -qreg-. > > 4. This final question relates to question 3. What would be the best > method for variable selection for my final model? Still would be > backwards elimination? How would I do this in stata, given the limited > availability of post estimation commmands? Just start with all > variables in my model, then eliminate ones with p-value greater than > 0.05 (or add ones with p-value less than 0.05 if I were to add stepwise > procedures too)? > >>>> The usual meta-comment is now that you won't get much support for > any flavour of stepwise on this list. Search the list archives for > "stepwise" and "Frank Harrell" for more. > > > * > * For searches and help try: > * http://www.stata.com/help.cgi?search > * http://www.stata.com/support/statalist/faq > * http://www.ats.ucla.edu/stat/stata/ _________________________________________________________________ So many new options, so little time. Windows Live Messenger. http://www.microsoft.com/windows/windowslive/products/messenger.aspx * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

**Follow-Ups**:**st: Computing local variance***From:*"Benjamin Villena Roldan" <bvillena@troi.cc.rochester.edu>

**References**:**st: Quantile regression with stata***From:*Tomas M <anon556656@live.ca>

**st: RE: Quantile regression with stata***From:*"Nick Cox" <n.j.cox@durham.ac.uk>

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